Erie: A Declarative Grammar for Data Sonification
ACM Human Factors in Computing Systems (CHI) 2024
An auditory histogram design using Erie. (A) An Erie specification for a histogram for Miles per Gallon variable in cars.json dataset. (B) Erie’s compiler computes the specification into an audio queue (time schedule for sounds to be played). (C) Erie’s web player renders the audio queue into actual sound.
Abstract
Data sonification—mapping data variables to auditory variables, such as pitch or volume—is used for data accessibility, scientific exploration, and data-driven art (e.g., museum exhibitions) among others. While a substantial amount of research has been made on effective and intuitive sonification design, software support is not commensurate, limiting researchers from fully exploring its capabilities. We contribute Erie, a declarative grammar for data sonification, that enables abstractly expressing auditory mappings. Erie supports specifying extensible tone designs (e.g., periodic wave, sampling, frequency/amplitude modulation synthesizers), various encoding channels, auditory legends, and composition options like sequencing and overlaying. Using standard Web Audio and Web Speech APIs, we provide an Erie compiler for web environments. We demonstrate the expressiveness and feasibility of Erie by replicating research prototypes presented by prior work and provide a sonification design gallery. We discuss future steps to extend Erie toward other audio computing environments and support interactive data sonification.
Citation
BibTeX
@inproceedings{kim2024erie,
title={Erie: A declarative grammar for data sonification},
author={Kim, Hyeok and Kim, Yea-Seul and Hullman, Jessica},
booktitle={Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems},
pages={1--19},
year={2024}
}